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» A Support Vector Clustering Method
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103
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ICPR
2008
IEEE
15 years 10 months ago
Adaptive asymmetrical SVM and genetic algorithms based iris recognition
We propose Genetic Algorithms to improve the feature subset selection by combining the valuable outcomes from multiple feature selection methods. This paper also motivates the use...
Kaushik Roy 0002, Prabir Bhattacharya
121
Voted
BMCBI
2008
93views more  BMCBI 2008»
15 years 3 months ago
Hybrid MM/SVM structural sensors for stochastic sequential data
In this paper we present preliminary results stemming from a novel application of Markov Models and Support Vector Machines to splice site classification of Intron-Exon and Exon-I...
Brian Roux, Stephen Winters-Hilt
109
Voted
SDM
2010
SIAM
151views Data Mining» more  SDM 2010»
15 years 5 months ago
Fast Stochastic Frank-Wolfe Algorithms for Nonlinear SVMs
The high computational cost of nonlinear support vector machines has limited their usability for large-scale problems. We propose two novel stochastic algorithms to tackle this pr...
Hua Ouyang, Alexander Gray
137
Voted
CORR
2008
Springer
159views Education» more  CORR 2008»
15 years 3 months ago
Face Detection Using Adaboosted SVM-Based Component Classifier
: Boosting is a general method for improving the accuracy of any given learning algorithm. In this paper we employ combination of Adaboost with Support Vector Machine (SVM) as comp...
Seyyed Majid Valiollahzadeh, Abolghasem Sayadiyan,...
126
Voted
TKDE
2008
123views more  TKDE 2008»
15 years 3 months ago
Explaining Classifications For Individual Instances
We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities...
Marko Robnik-Sikonja, Igor Kononenko